A Novel method for Schizophrenia classification using nonlinear features and neural networks

• Main Author: SV, Hari Prasad
• Format: Journal Article
• Language: English
• Published: 30-12-2023
• Subjects: Physics - Biological Physics; Quantitative Biology - Neurons and Cognition
• DOI: 10.48550/arxiv.2402.14819

One notable method for recording brainwaves to identify neurological problems
is electroencephalography (hereafter EEG). A trained neuro physician can learn
more about how the brain functions through the use of EEGs. However
conventionally, EEGs are only used to examine neurological problems (Eg.
Seizures). But abnormal links to neurological circuits can also exist in
psychological illnesses like Schizophrenia. Hence EEGs can be an alternate
source of data for detection and classification of psychological disorders. A
study on the classification of EEG data obtained from healthy individuals and
individuals experiencing schizophrenia is conducted. The inherent nonlinear
nature of brain waves are made use for the dimensionality reduction of the
data. Nonlinear parameters such as Lyapunov exponent (LE) and Hurst exponent
(HE) were selected as essential features. The EEG data was obtained from the
openly available EEG database of MV. Lomonosov Moscow State university. To
perform Noise reduction of the data, a more recently developed Tunable Q factor
based wavelet transform (TQWT) is used . Finally for the classification, the 16
channel EEG time series is converted into spatial heatmaps using the
aforementioned features. A convolutional neural network (CNN) is designed and
trained with the modified data format for classification